Post-training refers to the stage that follows the initial model training process, focusing on refining, optimizing, and preparing the model for deployment in real-world scenarios. After a model has been trained—often through pre-training and fine-tuning—it may still benefit from additional...

In the context of natural language processing (NLP) and machine learning, perplexity is a metric used to evaluate the performance of language models. It measures how well a model predicts a sequence of words, with lower perplexity indicating better performance....

In machine learning and deep learning, a parameter refers to a variable within a model that the training process adjusts to optimize the model’s performance. Parameters are the core elements that determine how a model processes input data and produces...

Open source refers to software, datasets, or other resources that are made publicly available, allowing anyone to view, use, modify, and distribute the source code or content. Open source promotes collaboration, transparency, and innovation by fostering a community-driven approach to...

In the context of computing, data science, and machine learning, a node is a fundamental unit or point within a structure or system that represents an entity, data element, or computational function. Nodes are widely used in various fields such...

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It combines linguistics, machine learning, and computational techniques to process and analyze natural language data, such as text...

A neural network is a computational model inspired by the human brain, designed to recognize patterns and relationships within data. It consists of interconnected layers of nodes (or neurons) that process and transmit information to solve tasks like classification, prediction,...

Multimodal refers to machine learning systems that process and integrate information from multiple types of data, or “modalities,” such as text, images, audio, video, and structured data. Multimodal models aim to leverage the complementary strengths of different data types to...

MNIST(Modified National Institute of Standards and Technology) is a widely used benchmark dataset in machine learning, consisting of 70,000 grayscale images of handwritten digits (0–9). It is primarily used for training and testing image processing systems, particularly in tasks like...

MLOps(Machine Learning Operations) is a set of practices, tools, and methodologies designed to streamline the deployment, monitoring, and maintenance of machine learning (ML) models in production environments. It combines principles from DevOps, data engineering, and ML to ensure the scalable,...